Image-Based Sorghum Head Counting When You Only Look Once

• URL: http://arxiv.org/abs/2009.11929v3
• Date: Mon, 13 Jun 2022 15:47:28 GMT
• Title: Image-Based Sorghum Head Counting When You Only Look Once
• Abstract summary: Modern trends in digital agriculture have seen a shift towards artificial intelligence for crop quality assessment and yield estimation. In this work, we document how a parameter tuned single-shot object detection algorithm can be used to identify and count sorghum head from aerial drone images.
• Score: 16.43087660376697
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern trends in digital agriculture have seen a shift towards artificial intelligence for crop quality assessment and yield estimation. In this work, we document how a parameter tuned single-shot object detection algorithm can be used to identify and count sorghum head from aerial drone images. Our approach involves a novel exploratory analysis that identified key structural elements of the sorghum images and motivated the selection of parameter-tuned anchor boxes that contributed significantly to performance. These insights led to the development of a deep learning model that outperformed the baseline model and achieved an out-of-sample mean average precision of 0.95.

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